Sonnant case study | AI inside a national broadcaster

A national broadcaster generates an enormous volume of audio every day. Turning that audio into something usable, searchable transcripts, compliance records, ad verification, content the business could act on, was slow, manual and bound to people. The work was real, repetitive and expensive, and almost none of the intelligence inside it was being captured.

The approach — the N+1 sequence, before it had a name

Sonnant did not start as a product pitch. It started inside the work: understanding exactly how the audio was handled, where the hours went and where the value leaked. Then the workflow was codified, the data captured and the system built to run it.

UNDERSTAND

Inside the work

Mapped how audio became usable, and where the manual hours sat.

WORKFLOW + DATA

Codify & capture

Turned the manual process into an intelligent layer that captured every output.

EXECUTION

Run it at scale

The layer did the work continuously, at a fraction of the cost and time.

The result

Sonnant removed the equivalent of 16 roles of manual work and saved the business millions, while turning a cost centre into a source of structured, reusable intelligence. It was the clearest possible proof of the N+1 idea: find the workflow, capture the data, build the layer, and the value compounds.

The app was never the point. The work was.

Tony Simmons · Founder, N+1

The same pattern works in your industry.

Will you own the category — or be owned?